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While a link between inflammation and the development of neuropsychiatric disorders, including major depressive disorder (MDD) is supported by a growing body of evidence, little is known about the contribution of aberrant adaptive immunity in this context. Here, we conducted in-depth characterization of T cell phenotype and T cell receptor (TCR) repertoire in MDD. For this cross-sectional case–control study, we recruited antidepressant-free patients with MDD without any somatic or psychiatric comorbidities (n = 20), who were individually matched for sex, age, body mass index, and smoking status to a non-depressed control subject (n = 20). T cell phenotype and repertoire were interrogated using a combination of flow cytometry, gene expression analysis, and next generation sequencing. T cells from MDD patients showed significantly lower surface expression of the chemokine receptors CXCR3 and CCR6, which are known to be central to T cell differentiation and trafficking. In addition, we observed a shift within the CD4+ T cell compartment characterized by a higher frequency of CD4+CD25highCD127low/− cells and higher FOXP3 mRNA expression in purified CD4+ T cells obtained from patients with MDD. Finally, flow cytometry-based TCR Vβ repertoire analysis indicated a less diverse CD4+ T cell repertoire in MDD, which was corroborated by next generation sequencing of the TCR β chain CDR3 region. Overall, these results suggest that T cell phenotype and TCR utilization are skewed on several levels in patients with MDD. Our study identifies putative cellular and molecular signatures of dysregulated adaptive immunity and reinforces the notion that T cells are a pathophysiologically relevant cell population in this disorder.

Introduction

Major depressive disorder (MDD) affects approximately 15% of adults over their lifespan (1) and is among the top two leading causes of years lived with disability worldwide (2). The etiopathogenesis of MDD is multifactorial and many neurobiological systems have been implicated (3, 4). Despite advanced understanding of the pathophysiology, approximately 30% of patients do not respond—even after several treatment attempts—to current antidepressants, which mainly target monoaminergic neurotransmission (5, 6). This underlines the need for approaches that target mechanisms beyond monoamine modulation (7).

Recently, accumulating experimental animal and human data have highlighted the importance of aberrant immunity in the development of depression (8). Similarly, epidemiological data have linked infections and autoimmunity to the risk of the disorder (9, 10). Exploring immune alterations in MDD may, thus, have the potential to open new avenues for treatment, since the immune system could be more amendable by therapeutic modulation than targets in the central nervous system (CNS).

Much of the research on immune dysfunction in MDD has focused on activation of the innate immune system (8). However, intriguing new evidence suggests that the different branches of the immune system might be differentially affected in MDD with relative impairment of adaptive immune responses (11), although this has not yet been explored in a targeted way on a cellular level. Moreover, preclinical studies have implicated the presence of a co-evolutionary link between T cell responses and CNS function (12, 13) and it is now increasingly recognized that the T cell compartment regulates cognition and mood-related behaviors in experimental animals (14, 15). Along similar lines, aberrant T cell differentiation and attenuated reactivity to CNS-derived antigens were hypothesized to play a role in MDD pathophysiology (8, 16). In view of these putative “antigen selection” pressures (i.e., infections and skewed specificity), we hypothesized that peripheral blood T cells from MDD patients would exhibit an altered phenotype as well as a biased profile in the T cell receptor (TCR) repertoire compared to non-depressed controls.

Materials and Methods

Subjects

We enrolled antidepressant-free patients with MDD but no other psychiatric comorbidities as well as non-depressed volunteers, matched pairwise for sex, age, current smoking status (yes/no), and BMI. All participants were of European descent.

Inclusion criteria for MDD patients: (a) psychiatrist-confirmed diagnosis of MDD, single or recurrent, according to DSM-IV criteria; (b) a minimum score of 18 points on the Hamilton Rating Scale for Depression (HRSD); (c) age 18–65 years; (d) at least 8 weeks free of any psychiatric medication (e.g., antidepressants, antipsychotics, and mood stabilizers).

Inclusion criteria for non-depressed controls: (a) no current or lifetime mood disorder diagnosis and (b) a score ≤5 on the Quick Inventory of Depressive Symptoms-Self Report (QIDS-SR).

Exclusion criteria for all participants: (a) past or present self-reported diagnosis of a major medical condition, including chronic or acute inflammatory, metabolic, and neurological disorders; (b) Axes I or II comorbidities; (c) regular use of either prescribed, over-the-counter medication or illicit drugs; thus, any subject on anti-inflammatory drugs, cholesterol-reducing drugs, and other possibly immune-modifying agents was excluded (see Table S1 in Supplementary Material); (d) drinking of more than 100 g of alcohol per week; (e) basic blood laboratory test values deviating significantly from the normal range; (f) current adverse life events (e.g., divorce, loss of job, and illness in the family); (g) pregnancy or nursing; and (h) recent vaccination (within the past month). Hypothyroidism in euthyroid state through hormonal substitution and hypertension in normotensive state through antihypertensive medication did not represent exclusion criteria (see Table S1 in Supplementary Material).

Patients were recruited through our specialized depression out-patient clinic program at the Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf. Non-depressed controls were recruited from the same geographical region through advertisements and from the staff of the University Medical Center Hamburg-Eppendorf. All participants provided written informed consent before enrollment in the study. This study has been approved by the appropriate Ethics Review Committee (Ethik-Kommission der Ärztekammer Hamburg, Ethikvotum PV4161 and PV4719).

Clinical Assessments

All subjects underwent detailed clinical assessments, including medical history, current medication, and psychiatric comorbidities. Diagnosis was established with the Structured Clinical Interview for the DSM-IV-TR Axis I Disorders (SCID-I) and depression severity was assessed using the HDRS by experienced board-certified psychiatrists (Cüneyt Demiralay and Agorastos Agorastos).

Peripheral Blood Mononuclear Cells (PBMC) Isolation and Biobanking

We obtained approximately 30 ml of blood in S-Monovette K3 EDTA tubes and 7 ml in S-Monovette Serum-Gel tubes (Sarstedt). All samples were collected in the morning (8:00 a.m.). PBMCs were then isolated using a Ficoll–Hypaque gradient as described (17), aliquoted in RPMI containing 10% DMSO and 25% FCS at 1 × 107 cells/ml, gradually cooled down to −80°C in a Mr. Frosty for 18 h and stored in liquid nitrogen until assayed. A small amount of whole blood (50 µl) was used for total and differential leukocyte counts using a Coulter Ac·T Diff hematology analyzer (Beckman Coulter). All other assays were performed using cryopreserved PBMCs or serum with one subject from each group run in parallel to control systematic variation in reagents used.

Data were acquired using a BD FACS LSR II flow cytometer and the FACS Diva v6.2 operating software. At least 1 × 105 live lymphocytes were acquired from case–control samples during the same session and using the same acquisition settings. Variability of instrument performance was normalized by use of Cytometer Setup and Tracking beads (BD Biosciences). Data analysis and plotting were performed using FlowJo v10.0.8 (Tree Star).

Serum Immunoassays for CXCL10 and CXCL11

CXCL10 and CXCL11 in sera were assayed with a multiplex bead-based immunoassay LEGENDplex (Biolegend) according to manufacturer’s instructions. For data acquisition and analysis, a BD FACS LSR II flow cytometer and the LEGENDplex v7.0 data analysis software were used, respectively.

Serum Radioimmunoassays for ACTH and Cortisol

Stress hormone levels (ACTH and cortisol) were measured in sera obtained at 8:00 a.m. with commercially available radioimmunoassays (IBL IRMA and ICN Biomedicals RIA, respectively), according to manufacturer’s instructions.

Next Generation Sequencing of the TCRβ Repertoire

For sequencing of the CDR3 region in CD4+ T cell clones, we extracted total genomic DNA from negatively purified CD4+ T cells using the DNeasy Blood and Tissue Kit (Qiagen), according to manufacturer’s instructions. We then amplified and sequenced the CDR3 region of rearranged TCRβ genes, which were defined according to IMGT (22), using previously described immunosequencing protocols [Adaptive Biotechnologies (23)]. This provides an unbiased measurement of the frequencies of individual T cell clones. Raw sequence data were pre-processed by Adaptive Biotechnologies for PCR and sequencing error correction (23) and uploaded into the immunoSEQ® Analyzer. Post-analysis was conducted using VDJtools v1.0.7 (24). Sequencing data are available using the following URL: http://doi.org/10.21417/B7RG6H.

Statistical Analyses

All continuous variables are presented as median values with interquartile range, unless otherwise specified. Differences between patients and matched controls were tested for statistical significance using the paired Wilcoxon signed-rank test. For dichotomous variables, the McNemar’s test was used. Bivariate correlation analyses were conducted using Spearman’s rank correlation test. As a measure of Vβ repertoire skewing, we calculated the Gini-TCR index, as previously described (25). This index is a direct measure of TCR Vβ usage with higher values indicating higher clonality (i.e., less evenly distributed repertoire). All statistical analyses were performed using SPSS version 19 (IBM). A two-tailed p < 0.05 was considered significant and p < 0.10 was considered a trend. All graphs were made using GraphPad Prism v5.04, except for the pie charts in Figure 6, which were generated using the R package ggplot2.

Results

Descriptives

Clinical characteristics of the MDD and control groups can be found in Table 1. Patient level clinical characteristics including non-psychiatric medication are provided in Table S1 in Supplementary Material. Depression severity in the MDD group was moderate to severe. 18 MDD patients were antidepressant-naïve at the time of inclusion.

TABLE 1

Table 1. Clinical characteristics.

Circulating Leukocyte Numbers and Frequencies

In order to obtain a general overview of immune cell composition in the peripheral blood, we first analyzed the absolute counts and frequencies of major leukocyte subsets. We observed no significant differences in absolute counts of circulating granulocytes, monocytes, and lymphocytes (Figure 1A), frequencies of major T cell subsets (CD4+ or CD8+) or B cells (Figures 1B,C). In line with recent studies (26–28), natural killer (NK) cells showed a trend toward lower frequency in MDD patients (p = 0.062; Figure 1B). Here, group differences were mainly driven by the putatively regulatory CD56highCD16− NK subset (p = 0.018; Figure 1D).

Chemokine Receptor Expression CXCR3 and CCR6

To identify possible shifts in T cell phenotype, we next analyzed the expression of two chemokine receptors that are characteristic of effector T cell differentiation (CXCR3 and CCR6) (18). T cells of MDD patients showed significant reductions in the surface expression of both CXCR3 (p = 0.001; Figures 2A,B) and CCR6 (p = 0.033; Figures 2C,D). This was seen in both CD4+ and CD8+ T cell subsets for CXCR3 (Figure 2B) and mainly in CD4+ T cells for CCR6 (Figure 2D). In 17 out of our 20 case–control pairs, CXCR3 expression was lower in the MDD subjects.

Lower surface CXCR3 expression was not T cell-specific, as the percentage of CXCR3-expressing CD3− lymphocytes (non-T cells) was also found to be significantly lower in MDD patients (p = 0.001; Figure 3A). However, lower surface expression of CCR6 was confined to T cells, as we detected no difference in the frequency of CCR6-expressing CD3− lymphocytes between patients and controls (p = 0.60; Figure 3A).

Next, we conducted several analyses to explore potential mechanisms that could underlie the extensive loss of surface CXCR3 expression in MDD. We found no differences in total cellular levels of CXCR3 (i.e., surface and intracellular combined) in T cells and non-T cells (Figures 3B,C), suggesting different receptor turnover at the cell membrane in MDD.

T cells rapidly internalize CXCR3 when exposed to CXCL10 and CXCL11 (29) and there is evidence to suggest that CXCL10 is elevated in MDD (30). In our sample, serum CXCL10 tended to be higher in MDD, although this difference reached only a trend (p = 0.091; Figure 2E). Descriptively, CXCL11 was also elevated but this did not reach statistical significance (Figure 2E). However, the intercorrelation between the two chemokines was robust (Spearman’s rho = 0.862, p < 0.001).

CD3 stimulation can be an alternative mechanism accounting for downregulation of both CXCR3 and CCR6 from the cell surface (31). In line with this hypothesis, we observed higher median fluorescence intensity of CD3 expression in T cells of MDD patients (p = 0.001; Figure 2F). It has been previously described that upregulation of surface CD3 is associated with prolonged TCR engagement (32), which provided further rationale for our hypothesis that MDD patients may bear TCR repertoires of biased clonal composition (see T cell repertoire analyses).

T Cell Repertoire Analysis

In order to explore whether a skewed T cell phenotype in MDD is paralleled by a biased TCR expression profile, we examined TCR Vβ family distribution by flow cytometry-based analysis of the entire cohort (n = 40). Overall, the T cell repertoire of MDD patients showed a trend for higher Gini-TCR indices in CD4+ cells (p = 0.057; Figure 5A), indicating that the CD4+ TCR repertoire might be less evenly distributed in MDD. By contrast, no evidence for skewing of the CD8+ T cell repertoire was observed (p = 0.36; data not shown).

Among CD4+ T cells, we found significantly higher usage of the Vβ families 5.1 and 22 (unadjusted p = 0.003 and p = 0.040, respectively; Figure 5A) and lower usage of Vβ 11 (unadjusted p = 0.015; Figure 5A). Next, we sought to confirm these observations by using next-generation sequencing in a subgroup of five case–control pairs, which were chosen in a manner blind to chemokine receptor expression and Gini-TCR index differences (see Table S1 in Supplementary Material). Sequencing of the beta chain CDR3 region in purified CD4+ T cells confirmed the increased usage of Vβ 5.1 (TCRBV05-01) in the MDD group as compared to the control group (p = 0.040, one-tailed planned comparison; Figure 5B). Differences regarding Vβ 11 (TCRBV25-01) and Vβ 22 (TCRBV02-01) showed the same pattern as the flow cytometry analysis in the whole cohort but did not reach statistical significance (Figure 5B). The top five Vβ 5.1 T cell clones for each subject, which were all “private” clones, are displayed in Figure 6.

Association between Immune and Neuroendocrine Variables

In order to explore whether T cell alterations are associated with neuroendocrine dysregulation in MDD patients (34), we conducted correlation analyses between cellular immune measures found to differ between the MDD and control groups and serum levels of stress hormones (ACTH and cortisol). We found no associations of ACTH or cortisol levels with our T cell phenotype and TCR repertoire outcomes (all p > 0.15, data not shown).

Discussion

Here, we provide converging evidence from several cellular and molecular approaches that antidepressant-free MDD patients display biased T cell phenotype and TCR repertoire as compared to closely matched, non-depressed controls. Peripheral blood T cells from MDD patients showed a shift in the CD4+ T compartment toward Treg cells, paralleled by lower surface expression of the T helper differentiation-related chemokine receptors CXCR3 and CCR6. In addition, a less diverse TCR utilization profile was seen within the CD4+ but not the CD8+ T cell subset.

Our cellular immune markers did not show any significant associations with circulating stress hormone levels (ACTH, cortisol); therefore, changes in T cell phenotype and TCR repertoire are unlikely to simply represent an epiphenomenon of neuroendocrine dysregulation in MDD (34). Furthermore, given the range of group differences in surface chemokine receptor expression as well as shifts in CD4+ T cell phenotype, our observations probably reflect a skewing in T cell differentiation, rather than implicating single molecules in MDD pathophysiology. Nevertheless, it is worthwhile to consider the potential functional implications of some of the candidate markers we identify in this study.

CXCR3 is a chemokine receptor that is highly expressed on effector CD4+ and CD8+ T cells and grants them entry into otherwise restricted sites of Th1-type inflammation and infection (35). Although we have not tested the functional consequences of lower surface CXCR3 in MDD directly, it has been previously shown that ligand-induced internalization of this receptor is associated with abolished migratory capacity of both CD4+ and CD8+ human T cells toward a cognate ligand (36). Furthermore, animal studies have shown that CXCR3 enhances the ability of T cells to safeguard against infections (37–39). Whether or not T cells from patients with MDD have a functional impairment with regard to their ability to respond to and clear infections should be, thus, investigated in future studies.

Intriguingly, animal models have shown that virus-induced “sickness-behavior,” which bears symptomatic and immunological similarities to MDD (40, 41), depends on the CXCL10-CXCR3 axis (42). Therefore, lower expression of CXCR3 as reported here might directly be involved in MDD pathophysiology. Along similar lines, a post mortem study showed that CXCR3+ T cells can be found in the human CNS, hinting at a potential role of these cells for maintenance and/or immune surveillance in the brain (43). Whether or not lower surface expression of CXCR3 in peripheral blood T cells might interfere with these functions, however, remains speculative. Ideally, this should further be explored in appropriate animal models, where immune cell trafficking into the CNS as well as peripheral tissues can be monitored.

Another functional implication of our findings could be overly suppressed host T cell responses in MDD owing to a bias toward Treg differentiation. It is worth noting that similar phenomena have been observed in chronic viral infections (44) as well as in animal models of neuropsychiatric disorders (45, 46).

Strengths of our study include the detailed clinical characterization and homogeneity of our patient cohort with moderate to severe MDD but no other psychiatric diagnoses or major somatic comorbidities. Further minimizing potential confounds, our study used close pairwise matching of cases and controls for variables that likely have an impact on immune function, such as sex, age, BMI, and smoking status. Moreover, all participants were non/mild drinkers and we only included currently unmedicated and mostly antidepressant-naïve MDD patients, using a clear cut-off in depression severity (HDRS score of 18 or more).

However, some limitations of our study have to be considered. First, no data were available for other clinical variables that may have influenced immune function, e.g., physical activity and nutrition (47). All MDD subjects were untreated with antidepressants and only four participants (n = 2 in the MDD group and n = 2 in the control group) were receiving non-psychiatric medications that were allowed in the study. Having said that, it is not possible to assess and control all potential additional factors that might be associated with MDD in cross-sectional case–control studies in humans. Thus, future longitudinal studies will be required to better understand the factors linked to the immune abnormalities observed herein. Second, the strict matching and the additional exclusion criteria applied increased the clinical homogeneity of the MDD group, but also led to a comparatively small study sample. This obviously has implications for the generalizability of our findings.

Beyond the advantages of more homogeneous patient groups in research studies, even in this small sample, we quite closely replicated previous immunological findings in MDD [e.g., elevated serum levels of CXCL10 (30)]. Furthermore, our results are in line with a recent whole blood transcriptomic analysis, which identified lower CCR6 transcripts in both antidepressant-treated and antidepressant-free MDD cohorts (48) and our findings on lower NK cell frequency are consistent with lower expression of NK-related genes in MDD (26). Thus, we are confident that our well-characterized cohort is representative of MDD patients.

Our results on higher Treg frequency are consistent with recent reports showing a higher percentage of CD25+CD127lowCCR4+ Tregs in antidepressant-free depressed patients (28) and a positive association between the frequency of CD25highCD127low Tregs and depressive symptoms in older adults following an acute stressor (49). However, our results are in conflict with other previous studies indicating lower frequency of Tregs in MDD patients (27, 50). One possible explanation for this discrepancy could be differences in the clinical characteristics of the study samples (medication status, age, BMI). In addition, methodological differences in Treg definition could also have contributed to these discrepancies so that functional analyses of Treg suppressive capacity will be needed in the future to more specifically determine the role of Tregs in MDD.

In summary, we provide converging evidence from molecular and cellular analyses for a skewed T cell phenotype and CD4+ T cell repertoire in antidepressant-free MDD patients. These findings from our hypothesis-driven study should be confirmed in larger studies and expanded by employing unbiased systems biology approaches. It is important to note that besides MDD, other psychiatric disorders such as schizophrenia have been linked to immune alterations. In schizophrenia, many of the known risk genes are involved in immune regulation (51) and data from animal models, clinical studies, and epidemiology support a role of the immune system in its pathobiology (c.f. (52) for a recent review). Moreover, meta-analyses have confirmed changes in lymphocyte subset counts and frequencies (53) and cytokine levels (54). However, it should be noted that both on a genetic level (55) as well as with regard to immunological parameters such as cytokine levels (56), there is considerable overlap between major psychiatric disorders, including MDD, schizophrenia, and bipolar disorder. Thus, more work is needed to determine if any immune markers are specific to a given disorder or maybe linked to a specific symptom domain observed across diagnostic categories. Ultimately, this may have the potential to open new avenues for research toward an immunotherapy for MDD.

Ethics Statement

This study has been approved by the appropriate Ethics Review Committee (Ethik-Kommission der Ärztekammer Hamburg, Ethikvotum PV4161 and PV4719). All participants provided written informed consent before enrolment in the study.

Conflict of Interest Statement

KP, AW, CD, JE, AL, CR, CG, TS, LS, and KW have no potential conflicts of interest to disclose. PA has received research funding from the Deutsche Forschungsgemeinschaft (DFG). MF has received research funding from the Deutsche Forschungsgemeinschaft (DFG) and the Federal Ministry of Education and Research (BMBF). OP has received research funding from the Federal Ministry of Education and Research (BMBF). AA has received research funding from the Werner Otto Foundation. SG has received honoraria from Mylan GmbH and research funding from the Deutsche Forschungsgemeinschaft (DFG), the Federal Ministry of Education and Research (BMBF), the National MS Society (NMSS), the Werner Otto Foundation, and the European Commission. KC and MV have employment and equity ownership with Adaptive Biotechnologies.

Funding

This study was supported by a grant from the Werner Otto Stiftung Hamburg (grant no. WO15/88 to AA and SMG). The research leading to these results has received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement n°[268381] (to SMG). Part of this work was supported by the Federal Ministry of Education and Research (BMBF, grant 161A130 to SMG and grant 16GW0082 to OP) and the Deutsche Forschungsgemeinschaft (KFO296, GO1357/8-1 to SMG). KP was in part supported by the Greek Scholarships Foundation (IKY, bequest in memory of Maria Zaoussi). AL was supported by an EFIS/Immunology Letters Short-Term Fellowship and the South East Europe Cooperation, University of Hamburg. SMG is supported by a Heisenberg Professorship from the Deutsche Forschungsgemeinschaft (DFG, GO1357/5-1 and 5-2). Data analysis provided by Adaptive Biotechnologies.